Apple Ads Marketplace Product Manager - Ad Matching & Retrieval

At Apple, we work every day to create products that enrich people’s lives. The App Store and Apple Maps are trusted destinations for millions of users to discover apps, places, products, and services. Our advertising platform connects users with high-utility advertiser offerings while maintaining Apple’s uncompromising commitment to user privacy. The Apple Ads Marketplace team is seeking an experienced, deeply technical Product Manager to drive the next generation of our ad matching, search intent, and retrieval platform. In this role, you will define the product strategy and roadmap for how we match user intent to relevant advertiser offerings across the App Store, Apple Maps, and emerging search and conversational surfaces. You will partner closely with world-class ML research and engineering teams to build, train, fine-tune, and inference cutting-edge machine learning and Large Language Model (LLM) systems at massive scale.

Description

As the Product Manager for Ad Matching & Retrieval, you will shape how users discover relevant apps and services across Apple’s ecosystem: - Pioneer Next-Gen Ad Matching with LLMs: Lead the strategy to train and deploy transformer and LLM-based models for semantic matching, query intent extraction, query rewriting, and keyword-to-ad relevance across billions of daily requests. - Advance Multi-Surface Search Retrieval: Expand retrieval capabilities across the App Store, Apple Maps, and conversational surfaces, ensuring high recall of high-utility ads tailored to diverse user contexts. - Scale Real-Time & Offline Inference: Collaborate with client and server ML engineering teams to optimize retrieval pipelines to enable embedded based retrieval, keyword generation, ANN vector search, candidate pruning, while keeping to a strict serving latency. - Own the Matching Product Roadmap: Define the vision, key metrics (retrieval recall, coverage, CTR impact, advertiser ROI), and execution milestones for auto-targeting, and both lexical and semantic intent features. - Leverage Cross-Functional Apple Signals: Partner with teams across Apple to ethically integrate privacy-preserving signals, platform ontologies, and catalog embeddings to continuously enrich match quality. - Data-Driven Strategy & Deep Dives: Analyze marketplace health, auction drop-offs, and query coverage to uncover gaps and inform future modeling directions.

Minimum Qualifications

3+ years of technical product management experience, owning the full product lifecycle from concept to launch for machine learning or advertising systems. Hands-on experience with AI/ML systems, with an emphasis on training, fine-tuning, evaluating, and inferencing large-scale deep learning models and LLMs. Strong domain knowledge in search, information retrieval, or ad matching, including keyword expansion, semantic search, vector embeddings, dense retrieval (e.g., bi-encoders, ANN indexing), and query understanding. Experience with high-throughput, low-latency online inference architectures across client and cloud server environments. Strong technical and analytical foundation, including deep proficiency with SQL and data exploration in large-scale data warehouses. Outstanding written and verbal communication skills, with proven ability to translate complex AI/ML architectures into crisp PRDs, system diagrams, and executive strategy. Demonstrated leadership and cross-functional influence, adept at aligning engineering, applied research, business, and design stakeholders without formal authority. Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, Data Science, or equivalent practical experience.

Preferred Qualifications

Experience building ad marketplace matching retrieval systems, including auto-targeting, keyword targeting, and keyword generation. Practical understanding of multi-modal search and graph-based retrieval across diverse catalog types (e.g., App Store apps, Maps points of interest, local business entities). Track record of designing and running large-scale online A/B experiments for marketplace optimization.